tensorflow-sigmoid预测

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sat Sep 15 10:54:53 2018
@author: myhaspl
@email:[email protected]
糖尿病预测
"""
import tensorflow as tf
import os

trainCount=10000
inputNodeCount=8
validateCount=20
sampleCount=100
testCount=7
outputNodeCount=1

g=tf.Graph()
with g.as_default():

    def getWeights(shape,wname):
        weights=tf.Variable(tf.truncated_normal(shape,stddev=0.1),name=wname)
        return weights

    def getBias(shape,bname):
        biases=tf.Variable(tf.constant(0.1,shape=shape),name=bname)
        return biases

    def inferenceInput(x):
        result=tf.add(tf.matmul(x,w1),b1)   
        return result

    def inference(x):
        yp=inferenceInput(x)
        return tf.sigmoid(yp)

    def loss():
        yp=inferenceInput(x)
        return tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y,logits=yp))

    def train(learningRate,trainLoss,trainStep):
        trainOp=tf.train.AdadeltaOptimizer(learningRate).minimize(trainLoss,global_step=trainStep)
        return trainOp

    def evaluate(x):
        return tf.cast(inference(x)>0.5,tf.float32)

    def accuracy(x,y,count):
        yp=evaluate(x)
        return tf.reduce_mean(tf.cast(tf.equal(yp,y),tf.float32))

    def inputs(fileName,skipLines=1):
        #生成文件名队列
        fileNameQueue=tf.train.string_input_producer([fileName])
        #生成记录键值对
        reader=tf.TextLineReader(skip_header_lines=skipLines)
        key,value=reader.read(fileNameQueue)
        return value

    def getNextBatch(n,values):
        recordDefaults=[[1.],[1.],[1.],[1.],[1.],[1.],[1.],[1.],[1.]]
        decoded=tf.decode_csv(values,record_defaults=recordDefaults)
        pregnancies,glucose,bloodPressure,skinThickness,insulin,bmi,diabetespedigreefunction,age,outcome=tf.train.shuffle_batch(decoded,batch_size=n,capacity=1000,min_after_dequeue=1)    
        features=tf.transpose(tf.stack([pregnancies,glucose,bloodPressure,skinThickness,insulin,bmi,diabetespedigreefunction,age]))
        y=tf.transpose([outcome])
        return (features,y) 

    with tf.name_scope("inputSamples"): 
        samples=inputs(os.getcwd()+"/diabetes.csv",1)
        inputX,inputY=getNextBatch(sampleCount,samples)  
        with tf.name_scope("validateSamples"):
            validateInputX,validateInputY=getNextBatch(validateCount,samples)

    with tf.name_scope("testSamples"):  
        testSamples=inputs(os.getcwd()+"/diabetes_test.csv",1)
        testInputX,testInputY=getNextBatch(testCount,testSamples)

    with tf.name_scope("inputDatas"):
        x=tf.placeholder(dtype=tf.float32,shape=[None,inputNodeCount],name="input_x")
        y=tf.placeholder(dtype=tf.float32,shape=[None,outputNodeCount],name="input_y")

    with tf.name_scope("Variable"):
        w1=tf.Variable(tf.truncated_normal([inputNodeCount,1],name="w1",stddev=0.1))
        b1=tf.Variable(0.1,dtype=tf.float32,name="b1")       
        trainStep=tf.Variable(0,dtype=tf.int32,name="tcount",trainable=False)      

with tf.Session(graph=g) as sess:

    trainLoss=loss()
    accuracyOp=accuracy(validateInputX,validateInputY,validateCount)
    trainOp=train(0.025,trainLoss,trainStep)

    init=tf.global_variables_initializer()     
    sess.run(init)
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)

    while trainStep.eval()<trainCount: 
        sampleX=sess.run(inputX)
        sampleY=sess.run(inputY)
        sess.run(trainOp,feed_dict={x:sampleX,y:sampleY})
        nowStep=sess.run(trainStep)
        if nowStep%500==0:
            validate_acc=sess.run(accuracyOp)
            print "%d次后=>正确率%g"%(nowStep,validate_acc)
        if nowStep>trainCount:
            break

    print "测试样本正确率%g"%sess.run(accuracy(testInputX,testInputY,testCount))

    print sess.run(inputY)
    print sess.run(evaluate(inputX))
    coord.request_stop()
    coord.join(threads)